Embedding dimension of entity \(d\) : The role of entity embedding dimension \(d\)  in the knowledge graph is a general problem [1]}, [2]}, [3]}, [4]}. Based on this assumption, we give the performance of \(d\)  taking different values {4, 8, 16, 32, 64} in the URIR algorithm, and the experimental results are shown in table REF . We noticed that the performance of URIR increase with the \(d\)  growing, and reach the best performance when \(d\)  reaches a certain value. This result implies that a larger embedding dimension \(d\)  can significantly help to encode more useful information. However, with the further increase of \(d\) , the performance of some data sets decrease, such as job data sets. As shown in Table REF , the sparsity of the job graph is significantly smaller than the other two data of ml and yelp. Future research should focus on the influence of graph sparsity on the selection of embedding dimensions [3]}, [4]}. This result indicates that too large embedding dimensions may cause the entity to be overrepresent and introduce noise, which affects the performance of the algorithm.
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